
import pandas as pd
import numpy as np

from matplotlib import pyplot as plt
import seaborn as sns


df = pd.read_csv("forest_fires_dataset.csv")
attrib = pd.read_csv("attributes_forest_fires.csv")

df


attrib


df.describe()


df["month"].value_counts()

aug    184
sep    172
mar     54
jul     32
feb     20
jun     17
oct     15
apr      9
dec      9
may      2
jan      2
nov      1
Name: month, dtype: int64


df["day"].value_counts()

sun    95
fri    85
sat    84
mon    74
tue    64
thu    61
wed    54
Name: day, dtype: int64


df.hist(figsize=(14, 12), bins=20)
plt.show()


area = df["area"]

upper_lim = area.quantile(.95)
lower_lim = area.quantile(.05)

data_percentile = area[(area < upper_lim) & (area > lower_lim)]

print('Wejściowy rozmiar: ', area.shape[0])
print('Wyjściowy rozmiar: ', data_percentile.shape[0])
print('Pozostało: ', round(data_percentile.shape[0] / area.shape[0], 2) * 100, '%', sep='')

Wejściowy rozmiar:  517
Wyjściowy rozmiar:  244
Pozostało: 47.0%


# porzucenie wierszy z outlierami w area oraz przekształcenie logarytmicznie kolumny

df = df[ df["area"].isin(data_percentile) ] 
df["area"] = np.log1p(df["area"]) 

df

<ipython-input-9-e29723587456>:4: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df["area"] = np.log1p(df["area"])


sns.displot(df["area"])

plt.show()


sns.pairplot(df, y_vars="area", x_vars=df.columns.values[:5])
sns.pairplot(df, y_vars="area", x_vars=df.columns.values[5:10])
sns.pairplot(df, y_vars="area", x_vars=df.columns.values[10:])

plt.show()


corr = df.loc[:, "FFMC":].corr()

sns.set(rc={'figure.figsize':(12, 8.)})

sns.heatmap(corr, annot=True, linewidths=.75, cmap="YlGnBu")

plt.show()


from pandas_profiling import ProfileReport

df = pd.read_csv('forest_fires_dataset.csv')

ProfileReport(df, explorative=True)

	X	Y	month	day	FFMC	DMC	DC	ISI	temp	RH	wind	rain	area
0	7	5	mar	fri	86.2	26.2	94.3	5.1	8.2	51.0	6.7	0.0	0.00
1	7	4	oct	tue	90.6	35.4	669.1	6.7	18.0	33.0	0.9	0.0	0.00
2	7	4	oct	sat	90.6	43.7	686.9	6.7	14.6	33.0	1.3	0.0	0.00
3	8	6	mar	fri	91.7	33.3	77.5	9.0	8.3	97.0	4.0	0.2	0.00
4	8	6	mar	sun	89.3	51.3	102.2	9.6	11.4	99.0	1.8	0.0	0.00
...	...	...	...	...	...	...	...	...	...	...	...	...	...
512	4	3	aug	sun	81.6	56.7	665.6	1.9	27.8	32.0	2.7	0.0	6.44
513	2	4	aug	sun	81.6	56.7	665.6	1.9	21.9	71.0	5.8	0.0	54.29
514	7	4	aug	sun	81.6	56.7	665.6	1.9	21.2	70.0	6.7	0.0	11.16
515	1	4	aug	sat	94.4	146.0	614.7	11.3	25.6	42.0	4.0	0.0	0.00
516	6	3	nov	tue	79.5	3.0	106.7	1.1	11.8	31.0	4.5	0.0	0.00

	name	type	description
0	X	integer	x-axis spatial coordinate within the Montesinh...
1	Y	integer	y-axis spatial coordinate within the Montesinh...
2	month	string	month of the year: 'jan' to 'dec'
3	day	string	day of the week: 'mon' to 'sun'
4	FFMC	float	FFMC index from the FWI system: 18.7 to 96.20
5	DMC	float	DMC index from the FWI system: 1.1 to 291.3
6	DC	float	DC index from the FWI system: 7.9 to 860.6
7	ISI	float	ISI index from the FWI system: 0.0 to 56.10
8	temp	float	temperature in Celsius degrees: 2.2 to 33.30
9	RH	float	relative humidity in %: 15.0 to 100
10	wind	float	wind speed in km/h: 0.40 to 9.40
11	rain	float	outside rain in mm/m2 : 0.0 to 6.4
12	area	float	the burned area of the forest (in ha): 0.00 to...

	X	Y	FFMC	DMC	DC	ISI	temp	RH	wind	rain	area
count	517.000000	517.000000	517.000000	517.000000	517.000000	517.000000	517.000000	517.000000	517.000000	517.000000	517.000000
mean	4.669246	4.299807	90.644681	110.872340	547.940039	9.021663	18.889168	44.288201	4.017602	0.021663	12.847292
std	2.313778	1.229900	5.520111	64.046482	248.066192	4.559477	5.806625	16.317469	1.791653	0.295959	63.655818
min	1.000000	2.000000	18.700000	1.100000	7.900000	0.000000	2.200000	15.000000	0.400000	0.000000	0.000000
25%	3.000000	4.000000	90.200000	68.600000	437.700000	6.500000	15.500000	33.000000	2.700000	0.000000	0.000000
50%	4.000000	4.000000	91.600000	108.300000	664.200000	8.400000	19.300000	42.000000	4.000000	0.000000	0.520000
75%	7.000000	5.000000	92.900000	142.400000	713.900000	10.800000	22.800000	53.000000	4.900000	0.000000	6.570000
max	9.000000	9.000000	96.200000	291.300000	860.600000	56.100000	33.300000	100.000000	9.400000	6.400000	1090.840000

	X	Y	month	day	FFMC	DMC	DC	ISI	temp	RH	wind	rain	area
138	9	9	jul	tue	85.8	48.3	313.4	3.9	18.0	42.0	2.7	0.0	0.307485
139	1	4	sep	tue	91.0	129.5	692.6	7.0	21.7	38.0	2.2	0.0	0.357674
140	2	5	sep	mon	90.9	126.5	686.5	7.0	21.9	39.0	1.8	0.0	0.385262
141	1	2	aug	wed	95.5	99.9	513.3	13.2	23.3	31.0	4.5	0.0	0.438255
142	8	6	aug	fri	90.1	108.0	529.8	12.5	21.2	51.0	8.9	0.0	0.476234
...	...	...	...	...	...	...	...	...	...	...	...	...	...
505	1	2	aug	thu	91.0	163.2	744.4	10.1	26.7	35.0	1.8	0.0	1.916923
509	5	4	aug	fri	91.0	166.9	752.6	7.1	21.1	71.0	7.6	1.4	1.153732
510	6	5	aug	fri	91.0	166.9	752.6	7.1	18.2	62.0	5.4	0.0	0.357674
512	4	3	aug	sun	81.6	56.7	665.6	1.9	27.8	32.0	2.7	0.0	2.006871
514	7	4	aug	sun	81.6	56.7	665.6	1.9	21.2	70.0	6.7	0.0	2.498152

Forest fire dataset EDA¶

1. Wczytanie bibliotek i danych¶

1.1 Wczytanie bibliotek¶

1.2 Wczytanie danych¶

1.3 Opis i typy kolumn¶

2 Analiza¶

2.1 Rozkłady¶

2.3 Korelacje¶

4. Zautomatyzowana EDA¶